Myelodysplastic neoplasms dissected into indolent, leukaemic and unfavourable subtypes by computational clustering of haematopoietic stem and progenitor cells.

Myelodysplastic neoplasms (MDS) encompass haematological malignancies, which are characterised by dysplasia, ineffective haematopoiesis and the risk of progression towards acute myeloid leukaemia (AML). Myelodysplastic neoplasms are notorious for their heterogeneity: clinical outcomes range from a near-normal life expectancy to leukaemic transformation or premature death due to cytopenia. The Molecular International Prognostic Scoring System made progress in the dissection of MDS by clinical outcomes. To contribute to the risk stratification of MDS by immunophenotypic profiles, this study performed computational clustering of flow cytometry data of CD34+ cells in 67 MDS, 67 AML patients and 49 controls. Our data revealed heterogeneity also within the MDS-derived CD34+ compartment. In MDS, maintenance of lymphoid progenitors and megakaryocytic-erythroid progenitors predicted favourable outcomes, whereas expansion of granulocyte-monocyte progenitors increased the risk of leukaemic transformation. The proliferation of haematopoietic stem cells and common myeloid progenitors with downregulated CD44 expression, suggestive of impaired haematopoietic differentiation, characterised a distinct MDS subtype with a poor overall survival. This exploratory study demonstrates the prognostic value of known and previously unexplored CD34+ populations and suggests the feasibility of dissecting MDS into a more indolent, a leukaemic and another unfavourable subtype.

Immunophenotypic aberrant hematopoietic stem cells in myelodysplastic syndromes: a biomarker for leukemic progression.

Myelodysplastic syndromes (MDS) comprise hematological disorders that originate from the neoplastic transformation of hematopoietic stem cells (HSCs). However, discrimination between HSCs and their neoplastic counterparts in MDS-derived bone marrows (MDS-BMs) remains challenging. We hypothesized that in MDS patients immature CD34+CD38- cells with aberrant expression of immunophenotypic markers reflect neoplastic stem cells and that their frequency predicts leukemic progression. We analyzed samples from 68 MDS patients and 53 controls and discriminated HSCs from immunophenotypic aberrant HSCs (IA-HSCs) expressing membrane aberrancies (CD7, CD11b, CD22, CD33, CD44, CD45RA, CD56, CD123, CD366 or CD371). One-third of the MDS-BMs (23/68) contained IA-HSCs. The presence of IA-HSCs correlated with perturbed hematopoiesis (disproportionally expanded CD34+ subsets beside cytopenias) and an increased hazard of leukemic progression (HR = 25, 95% CI: 2.9-218) that was independent of conventional risk factors. At 2 years follow-up, the sensitivity and specificity of presence of IA-HSCs for predicting leukemic progression was 83% (95% CI: 36-99%) and 71% (95% CI: 58-81%), respectively. In a selected cohort (n = 10), most MDS-BMs with IA-HSCs showed genomic complexity and high human blast counts following xenotransplantation into immunodeficient mice, contrasting MDS-BMs without IA-HSCs. This study demonstrates that the presence of IA-HSCs within MDS-BMs predicts leukemic progression, indicating the clinical potential of IA-HSCs as a prognostic biomarker.

Characterization of myelodysplastic syndromes hematopoietic stem and progenitor cells using mass cytometry.

BACKGROUND: Myelodysplastic syndromes (MDS) at risk of transformation to acute myeloid leukemia (AML) are difficult to identify. The bone marrows of MDS patients harbor specific hematopoietic stem and progenitor cell (HSPC) abnormalities that may be associated with sub-types and risk-groups. Leukemia-associated characteristics of such cells may identify MDS patients at risk of progression to AML and provide insight in the pathobiology of MDS. METHODS: Bone marrow samples from healthy donors (n = 10), low risk (n = 12) and high risk (n = 13) MDS patients were collected, in addition, AML samples for 5 out of 6 MDS patients that progressed. Mass cytometry was applied to assess expression of stem cell subset and leukemia-associated immunophenotype markers. RESULTS: We analyzed the data using FlowSOM to cluster cells with similar expression of 10 commonly used stem cell markers. Metaclusters (n = 20) of these clusters represented populations of cells with a related phenotype, largely resembling known stem cell subsets. Within specific subsets, intra-cellular expression levels of pCREB, IkBα, or pS6 differed significantly between healthy bone marrow (HBM) and MDS or consecutive secondary AML samples. CD34, CD44, and CD49f expression was significantly increased in high risk MDS and AML-associated metaclusters. We identified MDS/sAML cells with aberrant phenotypes when compared to HBM. Such cells were observed in clusters of both primary MDS and secondary AML samples. CONCLUSIONS: High-dimensional mass cytometry and computational data analyses enabled characterization of HSPC subsets in MDS and identification of leukemia stem cell populations based on their immunophenotype. Stem cells in MDS that display leukemia-associated features may predict the risk of developing AML.

Computational flow cytometry as a diagnostic tool in suspected-myelodysplastic syndromes.

The diagnostic work-up of patients suspected for myelodysplastic syndromes is challenging and mainly relies on bone marrow morphology and cytogenetics. In this study, we developed and prospectively validated a fully computational tool for flow cytometry diagnostics in suspected-MDS. The computational diagnostic workflow consists of methods for pre-processing flow cytometry data, followed by a cell population detection method (FlowSOM) and a machine learning classifier (Random Forest). Based on a six tubes FC panel, the workflow obtained a 90% sensitivity and 93% specificity in an independent validation cohort. For practical advantages (e.g., reduced processing time and costs), a second computational diagnostic workflow was trained, solely based on the best performing single tube of the training cohort. This workflow obtained 97% sensitivity and 95% specificity in the prospective validation cohort. Both workflows outperformed the conventional, expert analyzed flow cytometry scores for diagnosis with respect to accuracy, objectivity and time investment (less than 2 min per patient).

Prognostic relevance of morphological classification models for myelodysplastic syndromes in an era of the revised International Prognostic Scoring System.

Numerous morphological classification models have been developed to organise the heterogeneous spectrum of myelodysplastic syndromes (MDS). While the 2008 update of the World Health Organisation (WHO) is the current standard, the publication of the revised International Prognostic Scoring System (IPSS-R) has illustrated the need for supplemental prognostic information. The aim of this study was to investigate whether morphological classification models for MDS - of both the French-American-British (FAB) group and WHO - provide reliable criteria for their classification into homogeneous and clinically relevant categories with prognostic relevance beyond the IPSS-R. We reclassified 238 MDS patients using each of the FAB, WHO 2001 and WHO 2008 criteria and studied classification categories in terms of clinical, haematological and cytogenetic features. Subsequently, we calculated prognostic scores using the IPSS-R and investigated whether the morphological classification models had significantly prognostic value in patients stratified by the IPSS-R and vice versa. By adopting the FAB, WHO 2001 and WHO 2008 classifications, MDS patients were organised into homogeneous categories with intrinsic prognostic information. However, whereas the morphological classification models showed no prognostic value beyond the IPSS-R, the IPSS-R had significant prognostic value beyond the FAB, WHO 2001 and WHO 2008 classifications. Even though morphological classification models for MDS might be clinically relevant from a prognostic point of view, their relevance in terms of risk stratification is evidently limited in light of the IPSS-R. Therefore, we suggest to stop the use of morphological classification models for MDS for risk stratification in routine clinical practice.